Breast cancer is a serious health problem, the American Cancer Society currently estimating that over 182,000 U.S. women are diagnosed with breast cancer each year. Early detection of breast cancer is of utmost importance. Although conventional x-ray mammography is still one of the best methods for detecting early forms of breast cancer, and is the modality approved by the U.S. Food and Drug Administration (FDA) to screen for breast cancer in women who do not show symptoms of breast disease, it is still possible for cancers to be missed by the radiologist reviewing the conventional x-ray mammograms. For example, for breasts that are high in dense fibroglandular content as compared to fat content, which is common for younger and/or smaller-breasted patients, conventional x-ray mammograms often contain saturated bright areas that can obscure cancerous conditions.
For these and other reasons, substantial attention and technological effort has been dedicated toward breast x-ray tomosynthesis, which is similar in many respects to conventional x-ray mammography except that, for any particular view such as the CC or MLO view, the x-ray source is no longer stationary, but instead rotates through a limited angle relative to the breast platform normal (e.g., −15 degrees to +15 degrees) while several projection images (e.g., 10-15 projection images) are acquired by the x-ray detector. The several projection images are then mathematically processed to yield a relatively high number (e.g., 40-60) of tomosynthesis reconstructed images, each corresponding to a different slice of breast tissue, which can then be examined by the radiologist. Whereas a particular cancerous lesion positioned within a region of dense fibroglandular tissue might have been obscured in a single conventional x-ray mammogram view, that lesion could be readily apparent within a set of tomosynthesis reconstructed images representative of individual slices through the dense fibroglandular tissue. Examples of breast x-ray tomosynthesis systems can be found in U.S. Pat. No. 5,872,828, U.S. Pat. No. 7,123,684, and U.S. Pat. No. 7,245,694, each of which is incorporated by reference herein.
Computer-aided detection (CAD) refers to the use of computers to analyze medical images to detect anatomical abnormalities therein. Sometimes used interchangeably with the term computer-aided detection are the terms computer-aided diagnosis, computer-assisted diagnosis, or computer-assisted detection. The outputs of CAD systems are sets of information sufficient to communicate the locations of anatomical abnormalities, or lesions, in a medical image, and can also include other information such as the type of lesion, degree of suspiciousness, and the like. Such CAD detections are most often communicated in the form of graphical annotations overlaid upon diagnostic-quality and/or reduced-resolution versions of the medical image. CAD results are mainly used by radiologists as “secondary reads” or secondary diagnosis tools. Some CAD implementations, however, have used CAD results in a “concurrent reading” context in which the radiologists look at the CAD results at the same time that they look at the images. Thousands of CAD systems for conventional x-ray mammography are now installed worldwide, and are used to assist radiologists in the interpretation of millions of mammograms per year. X-ray mammography CAD systems are described, for example, in U.S. Pat. No. 5,729,620, U.S. Pat. No. 5,815,591, U.S. Pat. No. 5,917,929, U.S. Pat. No. 6,014,452, U.S. Pat. No. 6,075,879, U.S. Pat. No. 6,301,378, and U.S. Pat. No. 6,574,357, each of which is incorporated by reference herein.
CAD-assisted reading of medical images can be particularly important in the context of x-ray tomosynthesis imaging, where the number of medical images to be read by the radiologist is substantially greater than for conventional x-ray mammography. Various proposals for achieving x-ray tomosynthesis CAD have been set forth, for example, in U.S. Pat. No. 6,748,044, U.S. Pat. No. 7,218,766, US 20070052700A1, and US20080025592A1, each of which is incorporated by reference herein.
However, x-ray tomosynthesis image data sets represent unique collections of information that can bring about many pitfalls in the implementation of automated abnormality detection routines thereon. For example, as compared to conventional x-ray mammography images, tomosynthesis projection images tend to suffer from high amounts of quantum noise because of the reduced x-ray dosages involved, especially in high-density tissue areas. Particularly in the context of automated microcalcification detection, the high amount of quantum noise can lead to a high false positive rate and/or substantial computational inefficiencies in eliminating false positives. As another example, consistent with the equivocal “tomosynthesis” moniker, tomosynthesis reconstructed images carry with them a substantial number of reconstruction-related artifacts that would not be present if they were truly “tomography” images, and these artifacts can also confound certain CAD algorithms that focus too heavily upon the tomosynthesis reconstructed image data.
One particular challenge in implementing x-ray tomosynthesis CAD relates to making an initial determination of which pixels in the tomosynthesis reconstructed data set are “pixels of interest” that deserve further consideration by the many complex and time-consuming downstream processes in the CAD algorithm, such as processes that group the pixels together, arrange those groups into candidate anomaly lists, and extensively process the candidate anomalies to generate the ultimate set of CAD detections. On the one hand, if the initial determination algorithm is too inclusive, the overall efficiency of the CAD algorithm can suffer as the downstream algorithms process the overly long candidate anomaly lists. On the other hand, if the initial determination algorithm is too exclusionary, then the overall sensitivity of the CAD algorithm is reduced, i.e., truly suspicious lesions might be missed because the associated pixels were discarded in the initial determination algorithm. Achieving sensitive yet specific initial identification of the “pixels of interest” is particularly challenging in view of the substantial quantum noise present in the tomosynthesis projection images, together with the substantial artifacts that may be present in the tomosynthesis reconstructed images. Other issues arise as would be apparent to a person skilled in the art in view of the present disclosure.